Hydrocarbon exploration and production involve numerous operations performed by multiple parties using a wide range of tools and technologies. The sheer volume of data generated by these operations can make it difficult to identify and separate useful data from redundant or outdated data. For example, data may be generated related to well sites, drilling rigs, boreholes, subsurface formations, and the like. Just the raw borehole log data from acquisition companies alone can typically include large amounts of extraneous or otherwise low-value curves.
Additionally, acquisition of certain types of data from multiple logging runs and multiple tools within those runs can lead to repeated, though not identical readings for those data types. Routine editing and interpretation of such data over time can lead to a proliferation of inconsistent data as different data technicians and interpreters use different assumptions or apply different software and techniques to the data. The result is that it may often be difficult for a user to identify the best data to use for further processing, plotting, modeling, and the like.
Attempts to mitigate the above problems have involved users creating special or preferred data sets referred to as “gold” data sets. These gold data sets are usually labeled or otherwise named in a way that makes it readily evident they are considered by the users to contain the current “best” data available for use in future work. A drawback of the above approach is that the process is largely manual in nature, requiring the user to make a subjective determination as to which data is the best available. This may lead to problems with currency in that the presumed gold data set may not always be updated in a timely manner as new data becomes available, with quality in that the rules used to identify the data may not always be explicit or consistently applied, and with the amount of work required in that it takes much time and effort to find and label the best available data.
Accordingly, a need exists for a way to create gold data sets that ensures currency and quality and also reduces the amount of work required while minimizing the manual nature of the process.